RANGER: 用于病理报告生成的稀疏门控专家混合模型与自适应检索重排序
RANGER: Sparsely-Gated Mixture-of-Experts with Adaptive Retrieval Re-ranking for Pathology Report Generation
AI总结：
RANGER通过稀疏门控专家混合模型和自适应检索重排序，提升病理报告生成的生成特化与语义对齐，实验表明在PathText-BRCA数据集上性能优于现有方法。
摘要：
病理报告生成仍然是一个相对未被充分研究的下游任务，主要由于全滑片图像（WSIs）的吉像素尺度和复杂的形态学异质性。现有病理报告生成框架通常采用Transformer架构，依赖同质解码器架构和静态知识检索整合。此类架构限制了生成的专业化，并可能在报告生成过程中引入噪声外部指导。为了解决这些限制，我们提出了RANGER，一种具有自适应检索重排序的稀疏门控专家混合（MoE）框架，用于病理报告生成。具体而言，我们将稀疏门控MoE整合到解码器中，同时结合噪声top-k路由和负载平衡正则化，以在各种诊断模式中实现动态专家专业化。此外，我们引入了一个自适应检索重排序模块，该模块在整合前选择性地细化从知识库检索到的记忆，减少噪声并基于视觉特征表示提高语义对齐。我们在PathText-BRCA数据集上进行了广泛的实验，并在标准自然语言生成度量上展示了与现有方法相比的一致改进。我们的完整RANGER模型在PathText数据集上达到最优性能，分别达到BLEU-1到BLEU-4分数为0.4598、0.3044、0.2036和0.1435，METEOR为0.1883，ROUGE-L为0.3038，验证了动态专家路由和自适应知识细化在语义基础的病理报告生成中的有效性。
英文摘要：
Pathology report generation remains a relatively under-explored downstream task, primarily due to the gigapixel scale and complex morphological heterogeneity of Whole Slide Images (WSIs). Existing pathology report generation frameworks typically employ transformer architectures, relying on a homogeneous decoder architecture and static knowledge retrieval integration. Such architectures limit generative specialization and may introduce noisy external guidance during the report generation process. To address these limitations, we propose RANGER, a sparsely-gated Mixture-of-Experts (MoE) framework with adaptive retrieval re-ranking for pathology report generation. Specifically, we integrate a sparsely gated MoE into the decoder, along with noisy top-$k$ routing and load-balancing regularization, to enable dynamic expert specialization across various diagnostic patterns. Additionally, we introduce an adaptive retrieval re-ranking module that selectively refines retrieved memory from a knowledge base before integration, reducing noise and improving semantic alignment based on visual feature representations. We perform extensive experiments on the PathText-BRCA dataset and demonstrate consistent improvements over existing approaches across standard natural language generation metrics. Our full RANGER model achieves optimal performance on PathText dataset, reaching BLEU-1 to BLEU-4 scores of 0.4598, 0.3044, 0.2036, and 0.1435, respectively, with METEOR of 0.1883, and ROUGE-L of 0.3038, validating the effectiveness of dynamic expert routing and adaptive knowledge refinement for semantically grounded pathology report generation.
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